Understanding how gene regulatory networks (GRNs) allow organisms to adapt to changes in their environment is at the heart of biological systems. However, such networks are typically visualized as a static web of interactions when in reality they are highly fluid and dynamic, which allows them to integrate multiple nutrient and environmental signals and generate a rapid, fine-tuned response. My ultimate goal is to capture and model dynamic regulatory networks. In this project, I will use new experimental and computational approaches to identify the elusive transient transcription factor (TF) target interactions in GRNs and interrogate their role in mediating rapid responses to nitrogen (N) signals in plants. The proof-of-principle for these studies is basic leucine zipper 1 (bZIP1) which mediates nutrient signals in plants and other eukaryotes, but the approach can be applied to any TF in any organism. My studies will exploit a cell-based system that will enable me to temporally perturb both a TF and the N signal it transduces in root cells - where the N-signal is first perceived - and to capture TF-target interactions within minutes of TF nuclear entry [2]. In Aim 1, transient TF- target interactions will be captured in isolated root cells using a new DNA-methylation fingerprinting technique in combination with time-series transcriptome profiling and chromatin immunopreciptiation. Next, in Aim 2, these transient TF-target interactions captured in isolated cells will be connected to downstream responses that occur in the whole plant, using a combined genomic and network inference approach called Network Walking. Finally, in Aim 3, important transcriptional feed-forward motifs identified in the N-response network will be validated, and their impact on nitrogen signaling will be assessed. This study will have implications on N-use efficiency and reducing environmental and human health hazards of N-fertilizer contamination of ground water. More broadly, the combined experimental and computational approach is generally applicable to rapidly analyze dynamic gene regulatory networks across eukaryotes.